Update README.md

README.md

@@ -1,8 +1,8 @@
 ---
 title: EmployeeAttritionClassification
-emoji: 🚀
-colorFrom: red
-colorTo: red
+emoji: 🏢
+colorFrom: indigo
+colorTo: blue
 sdk: docker
 app_port: 8501
 tags:
@@ -12,9 +12,35 @@ short_description: Employee Attrition Predictor using XGBoost.
 license: mit
 ---
 
-# Welcome to Streamlit!
+## Overview
+This app predicts the **probability of employee attrition (Attrition = 1)** using a trained **XGBoost classifier**.
+The model was evaluated using **ROC-AUC** and achieved strong validation performance.
 
-Edit `/src/streamlit_app.py` to customize this app to your heart's desire. :heart:
+## Files in this repo
+Place these files in the **repo root (same folder as `app.py`)**:
 
-If you have any questions, checkout our [documentation](https://docs.streamlit.io) and [community
-forums](https://discuss.streamlit.io).
+- `xgb_model.pkl` — trained XGBoost model
+- `feature_names.pkl` — list of training feature columns (order matters)
+- `threshold.pkl` — decision threshold (e.g., 0.35)
+
+## How to use the app
+### 1) Single prediction (form)
+- Enter numeric/ordinal feature values
+- Select one option for categorical groups (one-hot)
+- The app outputs:
+  - `Attrition probability`
+  - `Attrition prediction` using your saved threshold
+
+### 2) Batch prediction (CSV upload)
+Upload a CSV that is already in the **same feature format** as your training data
+(after preprocessing and one-hot encoding).
+The app will align columns automatically using `feature_names.pkl`.
+
+## Notes
+- Kaggle submissions typically require **probabilities**, not class labels.
+- If you want to change the decision threshold, update `threshold.pkl`.
+
+## Local run
+```bash
+pip install -r requirements.txt
+streamlit run app.py